The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise
Source: Dell'Acqua et al., NBER w33641 · 2025-04
We examine how artificial intelligence transforms the core pillars of collaboration—performance, expertise sharing, and social engagement—through a pre-registered field experiment with 776 professionals at Procter & Gamble, a global consumer packaged goods company. Working on real product innovation challenges, professionals were randomly assigned to work either with or without AI, and either individually or with another professional in new product development teams. Our findings reveal that AI significantly enhances performance: individuals with AI matched the performance of teams without AI, demonstrating that AI can effectively replicate certain benefits of human collaboration. Moreover, AI breaks down functional silos. Without AI, R&D professionals tended to suggest more technical solutions, while Commercial professionals leaned towards commercially-oriented proposals. Professionals using AI produced balanced solutions, regardless of their professional background. Finally, AI’s language-based interface prompted more positive self-reported emotional responses among participants, suggesting it can fulfill part of the social and motivational role traditionally offered by human teammates. Our results suggest that AI adoption at scale in knowledge work reshapes not only performance but also how expertise and social connectivity manifest within teams, compelling organizations to rethink the very structure of collaborative work.
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Research framings built by reading the notes related to this paper — the questions it feeds into.
Does AI-assisted research sacrifice exploration breadth for productivity gains? Does AI assistance help or harm professional skill development?- Why do junior engineers lose formative struggle when AI absorbs entry-level work?
- Does generative AI improve immediate task performance but not sustained independent work?
- Does generative AI push knowledge workers toward different types of tasks?
- Do gains from AI assistance disappear when workers complete tasks alone?
- Does benefit from AI partnership depend on the individual worker?
- Does generative AI narrow performance gaps between different professional backgrounds?
- Can individuals using AI match the output of teams without AI?
- Why do AI productivity gains emerge most when workers apply existing skills?
- Does AI-assisted work reduce time spent on coordination and communication?
- Are heavy AI users spending more time on solo work instead of collaboration?
- Does generative AI adoption shift work away from coordination tasks?
- Does AGI focus distract firms from developing task-creating AI innovations?
- Does AI adoption push knowledge work away from communication toward solo tool use?
- What specific training approaches help managers integrate AI into team workflows?
- Why do external partnerships outperform internal AI team builds?
- What evidence exists that collaborative AI systems actually improve team outcomes?
- Why do people treat AI systems as group members rather than just tools?